Enhancing the Network Performance of Wireless Sensor Networks on Meta-heuristic Approach: Grey Wolf Optimization

The sensing technology has brought all advancements in the human lives. Wireless sensor network (WSN) has proven to be a promising solution to acquire the information from the remote areas. However, the energy constraints of the sensor nodes have obstructed the widely spread application zone of WSN. There has been a great magnitude of efforts reported for acquiring the energy efficiency in WSN, these efforts varying from conventional approaches to the meta-heuristic method for enhancing the network performance. In this paper, we have presented a comparative evaluation of state of art meta-heuristic approaches that helps in acquiring energy efficiency in the network. We have proposed grey wolf optimization (GWO-P) algorithm with the empirical analysis of the existing methods PSO, GA and WAO that will help the readers to select the appropriate approach for their applications. It is similarly exposed that in different other execution measurements GWO-P beats the contender calculations for length of stability, network lifetime, expectancy and so on.